{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "00695447",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Memory in LLMChain\n",
    "\n",
    "This notebook goes over how to use the Memory class with an `LLMChain`. \n",
    "\n",
    "We will add  the [ConversationBufferMemory](https://api.python.langchain.com/en/latest/memory/langchain.memory.buffer.ConversationBufferMemory.html#langchain.memory.buffer.ConversationBufferMemory) class, although this can be any memory class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f1aaf47",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.chains import LLMChain\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain.prompts import PromptTemplate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b066ced",
   "metadata": {},
   "source": [
    "The most important step is setting up the prompt correctly. In the below prompt, we have two input keys: one for the actual input, another for the input from the Memory class. Importantly, we make sure the keys in the `PromptTemplate` and the `ConversationBufferMemory` match up (`chat_history`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5501eda",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "template = \"\"\"You are a chatbot having a conversation with a human.\n",
    "\n",
    "{chat_history}\n",
    "Human: {human_input}\n",
    "Chatbot:\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(\n",
    "    input_variables=[\"chat_history\", \"human_input\"], template=template\n",
    ")\n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f6566275",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "llm = OpenAI()\n",
    "llm_chain = LLMChain(\n",
    "    llm=llm,\n",
    "    prompt=prompt,\n",
    "    verbose=True,\n",
    "    memory=memory,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e2b189dc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
      "\n",
      "\n",
      "Human: Hi there my friend\n",
      "Chatbot:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' Hi there! How can I help you today?'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_chain.predict(human_input=\"Hi there my friend\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a902729f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
      "\n",
      "Human: Hi there my friend\n",
      "AI:  Hi there! How can I help you today?\n",
      "Human: Not too bad - how are you?\n",
      "Chatbot:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\" I'm doing great, thanks for asking! How are you doing?\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_chain.predict(human_input=\"Not too bad - how are you?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33978824-0048-4e75-9431-1b2c02c169b0",
   "metadata": {},
   "source": [
    "## Adding Memory to a chat model-based `LLMChain`\n",
    "\n",
    "The above works for completion-style `LLM`s, but if you are using a chat model, you will likely get better performance using structured chat messages. Below is an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae5309bb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.prompts import (\n",
    "    ChatPromptTemplate,\n",
    "    HumanMessagePromptTemplate,\n",
    "    MessagesPlaceholder,\n",
    ")\n",
    "from langchain.schema import SystemMessage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a237bbb8-e448-4238-8420-004e046ef84e",
   "metadata": {},
   "source": [
    "We will use the [ChatPromptTemplate](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html?highlight=chatprompttemplate) class to set up the chat prompt.\n",
    "\n",
    "The [from_messages](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html#langchain_core.prompts.chat.ChatPromptTemplate.from_messages) method creates a `ChatPromptTemplate` from a list of messages (e.g., `SystemMessage`, `HumanMessage`, `AIMessage`, `ChatMessage`, etc.) or message templates, such as the [MessagesPlaceholder](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.chat.MessagesPlaceholder.html#langchain_core.prompts.chat.MessagesPlaceholder) below.\n",
    "\n",
    "The configuration below makes it so the memory will be injected to the middle of the chat prompt, in the `chat_history` key, and the user's inputs will be added in a human/user message to the end of the chat prompt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9bb8cde1-67c2-4133-b453-5c34fb36ff74",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "prompt = ChatPromptTemplate.from_messages(\n",
    "    [\n",
    "        SystemMessage(\n",
    "            content=\"You are a chatbot having a conversation with a human.\"\n",
    "        ),  # The persistent system prompt\n",
    "        MessagesPlaceholder(\n",
    "            variable_name=\"chat_history\"\n",
    "        ),  # Where the memory will be stored.\n",
    "        HumanMessagePromptTemplate.from_template(\n",
    "            \"{human_input}\"\n",
    "        ),  # Where the human input will injected\n",
    "    ]\n",
    ")\n",
    "\n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f77e466-a1a3-4c69-a001-ac5b7a40e219",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "llm = ChatOpenAI()\n",
    "\n",
    "chat_llm_chain = LLMChain(\n",
    "    llm=llm,\n",
    "    prompt=prompt,\n",
    "    verbose=True,\n",
    "    memory=memory,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f9709647-be82-43d5-b076-2a7da344ce8a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mSystem: You are a chatbot having a conversation with a human.\n",
      "Human: Hi there my friend\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Hello! How can I assist you today, my friend?'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chat_llm_chain.predict(human_input=\"Hi there my friend\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bdf04ebe-525a-4156-a3a7-65fd2df8d6fc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mSystem: You are a chatbot having a conversation with a human.\n",
      "Human: Hi there my friend\n",
      "AI: Hello! How can I assist you today, my friend?\n",
      "Human: Not too bad - how are you?\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"I'm an AI chatbot, so I don't have feelings, but I'm here to help and chat with you! Is there something specific you would like to talk about or any questions I can assist you with?\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chat_llm_chain.predict(human_input=\"Not too bad - how are you?\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
